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@mmcky@najuzilu@choldgraf notebooks execute in the same workspace as the artefacts .
Regarding this issue, the artefacts are not the problem, they are properly created with ipython magics and accessed. The problem is the __pycache__ folder which usually gets created with every import statement. In this case, because of the artefacts import statements, I presume..
Now, jupyter-cache specifically checks for artefacts to be a file:-
And in this case, __pycache__ is also passed as an artefact, which breaks the system because it is a folder.
We will need to have some checks on what gets added to the array of paths passed as artefacts to jupyter-cache
Perhaps the best approach here is to add a filter on paths to skip known patterns like __pycache__. Is there a configuration file available for jupyter-cache that allows for these patterns to be defined at the user level?
I think I might have a fix for this here: #49 but I'm not totally sure. I don't have more time to work on it today but if anybody wants to take it over and its helpful, please do so
@mmcky @najuzilu @choldgraf notebooks execute in the same workspace as the
artefacts
.Regarding this issue, the artefacts are not the problem, they are properly created with
ipython magics
and accessed. The problem is the__pycache__
folder which usually gets created with everyimport statement
. In this case, because of the artefacts import statements, I presume..Now,
jupyter-cache
specifically checks for artefacts to be a file:-And in this case,
__pycache__
is also passed as an artefact, which breaks the system because it is a folder.We will need to have some checks on what gets added to the array of paths passed as artefacts to
jupyter-cache
Originally posted by @AakashGfude in executablebooks/MyST-NB#122 (comment)
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